Deteksi Dini Stroke Menggunakan Machine Learning
DOI:
https://doi.org/10.55123/insologi.v4i4.5590Keywords:
Stroke, Machine Learning, Xgboost, Early Detection, SMOTEENN, Medical ClassificationAbstract
Stroke is one of the leading causes of death and disability worldwide. Early detection of stroke risk is crucial to prevent more severe complications. This study aims to develop a stroke prediction model based on machine learning using an open dataset from Kaggle containing patients' medical and demographic information. Four machine learning algorithms were utilized and compared: AdaBoost, Gradient Boosting, LightGBM, and XGBoost. Data preprocessing steps included missing value imputation, categorical variable encoding, numerical feature normalization, and class balancing using the SMOTEENN method. Additionally, feature selection was performed using the Extra Trees algorithm to enhance model performance. The results showed that the XGBoost model delivered the best performance, achieving an accuracy of 97.16%, an F1-score of 97.49%, and an AUC of 99.75%. This model proved to be effective in detecting stroke cases and holds potential for integration into clinical decision support systems. The study concludes that a combination of modern boosting algorithms and optimal preprocessing techniques can yield a reliable stroke prediction system suitable for implementation in digital healthcare contexts.
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Augi, Esraa H., and Almabruk Sultan. 2024. “The Early Warning Signs of a Stroke: An Approach Using Machine Learning Predictions.” Journal of Computer and Communications 12(06): 59–71. doi:10.4236/jcc.2024.126005.
Azhar, Yufis, Aidia Khoiriyah Firdausy, and Putri Juli Amelia. 2022. “Perbandingan Algoritma Klasifikasi Data Mining Untuk Prediksi Penyakit Stroke.” SINTECH (Science and Information Technology) Journal 5(2): 191–97. doi:10.31598/sintechjournal.v5i2.1222.
Bugis, Haris. 2022. “Metode Naïve Bayes Untuk Memprediksi Penyakit Stroke.” Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) 6(1): 8–14. doi:10.47970/siskom-kb.v6i1.317.
Chung, Chen Chih, Emily Chia Yu Su, Jia Hung Chen, Yi Tui Chen, and Chao Yang Kuo. 2023. “XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke.” Diagnostics 13(5): 1–13. doi:10.3390/diagnostics13050842.
Fadillah Hermawan, Agiel, Fajri Rakhmat Umbara, and Fatan Kasyidi. 2022. “MIND (Multimedia Artificial Intelligent Networking Database Prediksi Awal Penyakit Stroke Berdasarkan Rekam Medis Menggunakan Metode Algoritma CART(Classification and Regression Tree).” Journal MIND Journal | ISSN 7(2): 151–64. https://doi.org/10.26760/mindjournal.v7i2.151-164.
Gupta, Aakanshi, Nidhi Mishra, Nishtha Jatana, Shaily Malik, Khaled A. Gepreel, Farwa Asmat, and Sachi Nandan Mohanty. 2024. “Predicting Stroke Risk: An Effective Stroke Prediction Model Based on Neural Networks.” Journal of Neurorestoratology 13(1): 100156. doi:10.1016/j.jnrt.2024.100156.
Hasibuan, M Said, Devi Fransisca, Jurusan Magister, Teknik Informatika, and Fakultas Ilmu Komputer. 2022. “Penggunaan Algoritma Naive Bayes Dan Particle Swarm Optimization ( PSO ) Untuk Mendeteksi Stroke.” : 109–18.
Herlistiono, Iwa Ovyawan, and Sriyani Violina. 2024. “Model Prediksi Risiko Stroke Menggunakan Machine Learning.” INTECOMS: Journal of Information Technology and Computer Science 7(4): 1230–38. doi:10.31539/intecoms.v7i4.10942.
Indah Werdiningsih, Endah Purwanti, Iin Mardiyana, Arum Tiyas Handayani, Kharristantie Sekarlangit Suryadewi, Endang Nurjanah, Fildzah Akhlaqulkarimah, Naurah Hedy Pramiyas, and Fakhrana Almas Syah Yahrani. 2023. “Analisis Prediksi Stroke Menggunakan Pendekatan Decision Tree Dengan Seleksi Fitur Dan Neural Network.” Jurnal Sistem Cerdas 6(3): 213–21. doi:10.37396/jsc.v6i3.310.
Iskandar, Nur Aliffiyanti, Iin Ernawati, and Yuni Widiastiwi. 2022. “Klasifikasi Diagnosis Penyakit Stroke Dengan Menggunakan Metode Random Forest.” Seminak Nasional Mahasiswa Ilmu Komputer dan Aplikasinya (SENAMIKA): 432–41. https://conference.upnvj.ac.id/index.php/senamika/article/view/2190.
Maskuri, Muhammad Naja, Kadek Sukerti, and R M Herdian Bhakti. 2022. “Penerapan Algoritma K-Nearest Neighbor (KNN) Untuk Memprediksi Penyakit Stroke Stroke Desease Predict Using KNN Algorithm.” Jurnal Ilmiah Intech : Information Technology Journal of UMUS 4(1): 130–40.
Putra, Leonardus Sandy Ade, Eka Kusumawardhani, Putranty Widha Nugraheni, Lalak Tarbiyatun Nasyin Maleiva, and Vincentius Abdi Gunawan. 2022. “Sistem Identifikasi Dini Penyakit Stroke Dengan Menggunakan Jaringan Syaraf Tiruan Perambatan Balik.” Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika 16(2): 145–57. doi:10.47111/jti.v16i2.5096.
Rahim, Abd Mizwar A, Andi Sunyoto, and Muhammad Rudyanto Arief. 2022. “Stroke Prediction Using Machine Learning Method with Extreme Gradient Boosting Algorithm.” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 21(3): 595–606. doi:10.30812/matrik.v21i3.1666.
Salsabila, Mutiara, Novi Susanti, Niken Natani Sabilla, Henny Irene, Natalia Hulu, Program Studi, Ilmu Kesehatan, et al. 2024. “Analisis Kebijakan Kesehatan Tentang Pencegahan Dan Penanganan Stroke Di Indonesia.” 3(2): 931–38.
Swathi Priyadarshini, T., and Mohd Abdul Hameed. 2024. “Developing Heart Stroke Prediction Model Using Deep Learning with Combination of Fixed Row Initial Centroid Method with Navie Bayes, Decision Tree, and Artificial Neural Network.” Measurement: Sensors 34(May): 101237. doi:10.1016/j.measen.2024.101237.
Wulandari, Serin, Yogi Isro’Mukti, and Tri Susanti. 2024. “Optimalisasi Prediksi Penyakit Stroke Menggunakan Algoritma Deep Learning.” JATI (Jurnal Mahasiswa Teknik Informatika) 8(2): 1826–33. doi:10.36040/jati.v8i2.9256.
Zuama, Robi Aziz, Syaifur Rahmatullah, and Yuri Yuliani. 2022. “Analisis Performa Algoritma Machine Learning Pada Prediksi Penyakit Cerebrovascular Accidents.” Jurnal Media Informatika Budidarma 6(1): 531. doi:10.30865/mib.v6i1.3488.
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